This document assumes some familiarity with contributing to open source scientific Python projects using GitHub pull requests. If this does not describe you, you may first want to see the New Contributor FAQ.
If you are a first-time contributor:
Go to networkx/networkx and click the “fork” button to create your own copy of the project.
Clone the project to your local computer:
git clone email@example.com:your-username/networkx.git
Navigate to the folder networkx and add the upstream repository:
git remote add upstream firstname.lastname@example.org:networkx/networkx.git
Now, you have remote repositories named:
upstream, which refers to the
origin, which refers to your personal fork
Next, you need to set up your build environment. Here are instructions for two popular environment managers:
# Create a virtualenv named ``networkx-dev`` that lives in the directory of # the same name python -m venv networkx-dev # Activate it source networkx-dev/bin/activate # Install main development and runtime dependencies of networkx pip install -r requirements/default.txt -r requirements/test.txt -r requirements/developer.txt # # (Optional) Install pygraphviz and pydot packages # These packages require that you have your system properly configured # and what that involves differs on various systems. # pip install -r requirements/extra.txt # # Build and install networkx from source pip install -e . # Test your installation pytest --pyargs networkx
conda(Anaconda or Miniconda)
# Create a conda environment named ``networkx-dev`` conda create --name networkx-dev # Activate it conda activate networkx-dev # Install main development and runtime dependencies of networkx conda install -c conda-forge --file requirements/default.txt --file requirements/test.txt --file requirements/developer.txt # # (Optional) Install pygraphviz and pydot packages # These packages require that you have your system properly configured # and what that involves differs on various systems. # conda install -c conda-forge --file requirements/extra.txt # # Install networkx from source pip install -e . # Test your installation pytest --pyargs networkx
Finally, we recommend you use a pre-commit hook, which runs black when you type
Develop your contribution:
Pull the latest changes from upstream:
git checkout main git pull upstream main
Create a branch for the feature you want to work on. Since the branch name will appear in the merge message, use a sensible name such as ‘bugfix-for-issue-1480’:
git checkout -b bugfix-for-issue-1480 main
Commit locally as you progress (
Test your contribution:
Run the test suite locally (see Testing for details):
PYTHONPATH=. pytest networkx
Running the tests locally before submitting a pull request helps catch problems early and reduces the load on the continuous integration system.
Submit your contribution:
Push your changes back to your fork on GitHub:
git push origin bugfix-for-issue-1480
Go to GitHub. The new branch will show up with a green Pull Request button—click it.
If you want, post on the mailing list to explain your changes or to ask for review.
Every Pull Request (PR) update triggers a set of continuous integration services that check that the code is up to standards and passes all our tests. These checks must pass before your PR can be merged. If one of the checks fails, you can find out why by clicking on the “failed” icon (red cross) and inspecting the build and test log.
Reviewers (the other developers and interested community members) will write inline and/or general comments on your PR to help you improve its implementation, documentation, and style. Every single developer working on the project has their code reviewed, and we’ve come to see it as friendly conversation from which we all learn and the overall code quality benefits. Therefore, please don’t let the review discourage you from contributing: its only aim is to improve the quality of project, not to criticize (we are, after all, very grateful for the time you’re donating!).
To update your PR, make your changes on your local repository and commit. As soon as those changes are pushed up (to the same branch as before) the PR will update automatically.
If the PR closes an issue, make sure that GitHub knows to automatically close the issue when the PR is merged. For example, if the PR closes issue number 1480, you could use the phrase “Fixes #1480” in the PR description or commit message.
If your change introduces any API modifications, please update
To set up a function for deprecation:
Use a deprecation warning to warn users. For example:
msg = "curly_hair is deprecated and will be removed in v3.0. Use sum() instead." warnings.warn(msg, DeprecationWarning)
Add a warning to
warnings.filterwarnings( "ignore", category=DeprecationWarning, message=<start of message> )
Add a reminder to
doc/developer/deprecations.rstfor the team to remove the deprecated functionality in the future. For example:
* In ``utils/misc.py`` remove ``generate_unique_node`` and related tests.
Add a note (and a link to the PR) to
[`#4281 <https://github.com/networkx/networkx/pull/4281>`_] Deprecate ``read_yaml`` and ``write_yaml``.
To reviewers: make sure the merge message has a brief description of the change(s) and if the PR closes an issue add, for example, “Closes #123” where 123 is the issue number.
If GitHub indicates that the branch of your Pull Request can no longer be merged automatically, merge the main branch into yours:
git fetch upstream main git merge upstream/main
If any conflicts occur, they need to be fixed before continuing. See which files are in conflict using:
Which displays a message like:
Unmerged paths: (use "git add <file>..." to mark resolution) both modified: file_with_conflict.txt
Inside the conflicted file, you’ll find sections like these:
<<<<<<< HEAD The way the text looks in your branch ======= The way the text looks in the main branch >>>>>>> main
Choose one version of the text that should be kept, and delete the rest:
The way the text looks in your branch
Now, add the fixed file:
git add file_with_conflict.txt
Once you’ve fixed all merge conflicts, do:
Advanced Git users may want to rebase instead of merge, but we squash and merge PRs either way.
All code should have tests.
All code should be documented, to the same standard as NumPy and SciPy.
All changes are reviewed. Ask on the mailing list if you get no response to your pull request.
Default dependencies are listed in
requirements/default.txtand extra (i.e., optional) dependencies are listed in
requirements/extra.txt. We don’t often add new default and extra dependencies. If you are considering adding code that has a dependency, you should first consider adding a gallery example. Typically, new proposed dependencies would first be added as extra dependencies. Extra dependencies should be easy to install on all platforms and widely-used. New default dependencies should be easy to install on all platforms, widely-used in the community, and have demonstrated potential for wide-spread use in NetworkX.
Use the following import conventions:
import numpy as np import scipy as sp import matplotlib as mpl import matplotlib.pyplot as plt import pandas as pd import networkx as nx
import scipy as sp
use the following imports:
import scipy.linalg # call as sp.linalg import scipy.sparse # call as sp.sparse import scipy.sparse.linalg # call as sp.sparse.linalg import scipy.stats # call as sp.stats import scipy.optimize # call as sp.optimize
For example, many libraries have a
sp.sparse.linalg. The above import pattern makes the origin of any particular instance of
Use the decorator
networkx/utils/decorators.pyto designate that a function doesn’t accept ‘directed’, ‘undirected’, ‘multigraph’ or ‘graph’. The first argument of the decorated function should be the graph object to be checked.
@nx.not_implemented_for("directed", "multigraph") def function_not_for_MultiDiGraph(G, others): # function not for graphs that are directed *and* multigraph pass @nx.not_implemented_for("directed") @nx.not_implemented_for("multigraph") def function_only_for_Graph(G, others): # function not for directed graphs *or* for multigraphs pass
networkx has an extensive test suite that ensures correct
execution on your system. The test suite has to pass before a pull
request can be merged, and tests should be added to cover any
modifications to the code base.
We make use of the pytest
testing framework, with tests located in the various
To run all tests:
$ PYTHONPATH=. pytest networkx
Or the tests for a specific submodule:
$ PYTHONPATH=. pytest networkx/readwrite
Or tests from a specific file:
$ PYTHONPATH=. pytest networkx/readwrite/tests/test_edgelist.py
Or a single test within that file:
$ PYTHONPATH=. pytest networkx/readwrite/tests/test_edgelist.py::test_parse_edgelist_with_data_list
--doctest-modules to run doctests.
For example, run all tests and all doctests using:
$ PYTHONPATH=. pytest --doctest-modules networkx
Tests for a module should ideally cover all code in that module, i.e., statement coverage should be at 100%.
To measure the test coverage, run:
$ PYTHONPATH=. pytest --cov=networkx networkx
This will print a report with one line for each file in
detailing the test coverage:
Name Stmts Miss Branch BrPart Cover ---------------------------------------------------------------------------------- networkx/__init__.py 33 2 2 1 91% networkx/algorithms/__init__.py 114 0 0 0 100% networkx/algorithms/approximation/__init__.py 12 0 0 0 100% networkx/algorithms/approximation/clique.py 42 1 18 1 97% ...
If you’re new to testing, see existing test files for examples of things to do. Don’t let the tests keep you from submitting your contribution! If you’re not sure how to do this or are having trouble, submit your pull request anyway. We will help you create the tests and sort out any kind of problem during code review.
To run image comparisons:
$ PYTHONPATH=. pytest --mpl --pyargs networkx.drawing
pytest to use
pytest-mpl to compare the generated plots
with baseline ones stored in
To add a new test, add a test function to
returns a Matplotlib figure (or any figure object that has a savefig method)
and decorate it as follows:
@pytest.mark.mpl_image_compare def test_barbell(): fig = plt.figure() barbell = nx.barbell_graph(4, 6) # make sure to fix any randomness pos = nx.spring_layout(barbell, seed=42) nx.draw(barbell, pos=pos) return fig
Then create a baseline image to compare against later:
$ pytest -k test_barbell --mpl-generate-path=networkx/drawing/tests/baseline
$ pytest -k test_barbell --mpl
Building the documentation locally requires that the additional dependencies
requirements/doc.txt be installed in your development
The documentation is built with
sphinx. To build the documentation locally,
navigate to the
doc/ directory and:
This will generate both the reference documentation as well as the example gallery. If you want to build the documentation without building the gallery examples use:
The build products are stored in
doc/build/ and can be viewed directly.
For example, to view the built html, open
in your preferred web browser.
The gallery examples are managed by
The source files for the example gallery are
.py scripts in
generate one or more figures. They are executed automatically by sphinx-gallery when the
documentation is built. The output is gathered and assembled into the gallery.
Building the example gallery locally requires that the additional dependencies
requirements/example.txt be installed in your development environment.
You can add a new plot by placing a new
.py file in one of the directories inside the
examples directory of the repository. See the other examples to get an idea for the
Gallery examples should start with
General guidelines for making a good gallery plot:
Examples should highlight a single feature/command.
Try to make the example as simple as possible.
Data needed by examples should be included in the same directory and the example script.
Add comments to explain things are aren’t obvious from reading the code.
Describe the feature that you’re showcasing and link to other relevant parts of the documentation.
If you are contributing a new algorithm (or an improvement to a current algorithm),
a reference paper or resource should also be provided in the function docstring.
For references to published papers, we try to follow the
Chicago Citation Style.
The quickest way of generating citation in this style is
by searching for the paper on Google Scholar and clicking on
cite button. It will pop up the citation of the paper in multiple formats, and copy the
We prefer adding DOI links for URLs. If the DOI link resolves to a paywalled version of the article, we prefer adding a link to the arXiv version (if available) or any other publicly accessible copy of the paper.
An example of a reference:
..  Cheong, Se-Hang, and Yain-Whar Si. "Force-directed algorithms for schematic drawings and placement: A survey." Information Visualization 19, no. 1 (2020): 65-91. https://doi.org/10.1177%2F1473871618821740
If the resource is uploaded as a PDF/DOCX/PPT on the web (lecture notes, presentations) it is better to use the wayback machine to create a snapshot of the resource and link the internet archive link. The URL of the resource can change, and it creates unreachable links from the documentation.
Please report bugs on GitHub.
All interactions with the project are subject to the NetworkX code of conduct.
We also follow these policies: